Logforum
Wyższa Szkoła Logistyki
1895-2038
1734-459X
The impact of Lean & Green Supply Chain Practices on Sustainability: Literature Review and Conceptual Framework
ORIGINAL_ARTICLE
1-13
en
2022
18
1
Ait Hammou
Ikram
Oulfarsi
Salah
Hebaz
Ali
Abualfaraa W., Salonitis K., Al-Ashaab A., & Ala’raj M., 2020. Lean-green manufacturing practices and their link with sustainability: A critical review. Sustainability, 12(3), 1–21. https://doi.org/10.3390/su12030981 Alqudah S., Shrouf H., Suifan T., & Alhyari S., 2020. A moderated mediation model of lean, agile, resilient, and green paradigms in the supply chain. International Journal of Supply Chain Management, 9(4), 158–172. Azevedo S. G., Carvalho H., & Cruz Machado V., 2011. The influence of green practices on supply chain performance: A case study approach. Transportation Research Part E: Logistics and Transportation Review, 47(6), 850–871. https://doi.org/10.1016/j.tre.2011.05.017 Azevedo S. G., Carvalho H., Duarte S., & Cruz-Machado V., 2012. Influence of Green and Lean Upstream Supply Chain Management Practices on Business Sustainability. IEEE Transactions on Engineering Management, 59(4), 753–765. https://doi.org/10.1007/11548669_27 Campos L. M., & Vazquez-Brust D., 2016. Lean and Green Synergies in Supply Chain Management. Supply Chain Management: An International Journal, 21(5). https://doi.org/10.1108/SCM-03-2016- 0101 Carvalho H., Govindan K., Azevedo S. G., & Cruz-Machado V., 2017. Modelling green and lean supply chains: An eco-efficiency perspective. Resources, Conservation and Recycling, 120, 75–87. https://doi.org/10.1016/j.resconrec.2016.09 .025 Carvalho H., Azevedo S. G., & Cruz-Machado V., 2010. Supply chain performance management: lean and green paradigms. International Journal of Business Performance and Supply Chain Modelling, 2(3-4), 304-333. Cherrafi A., Elfezazi S., Hurley B., Garza Reyes J. A., Kumar V., Anosike A., & Batista L., 2019. Green and lean: a Gemba– Kaizen model for sustainability enhancement. Production Planning and Control, 30(5–6), 385–399. https://doi.org/10.1080/09537287.2018.15 01808 Cherrafi A., Garza-Reyes J. A., Kumar V., Mishra N., Ghobadian A., & Elfezazi S. 2018. Lean, green practices and process innovation: A model for green supply chain performance. International Journal of Production Economics, 206, 79-92. https://doi.org/10.1016/j.ijpe.2018.09.031 Duarte S., & Cruz-Machado V., 2015. Investigating lean and green supply chain linkages through a balanced scorecard framework. International Journal of Management Science and Engineering Management, 10(1), 20-29. https://doi.org/10.1080/17509653.2014.96 2111 Duarte S., & Cruz-Machado V., 2019. Green and lean supply-chain transformation: a roadmap. Production Planning & Control, 30(14), 1170-1183. https://doi.org/10.1080/09537287.2019.15 95207 Dües C. M., Tan K. H., & Lim M., 2013. Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain. Journal of cleaner production, 40, 93-100. https://doi.org/10.1016/j.jclepro.2011.12.0 23 Engin B. E., Martens M., & Paksoy T., 2019. Lean and Green Supply Chain Management: A Comprehensive Review. Lean and Green Supply Chain Management, 1-38. https://do
Lean Practices, Green Practices, Supply Chain Management, sustainable performance, Conceptual Framework
10.17270/J.LOG.2022.684
Green concepts in the supply chain
ORIGINAL_ARTICLE
15-25
en
2022
18
1
Piotr
Sosnowski
Bai, C., Sarkis, J., 2010, Green supplier development: Analytical evaluation using rough set theory, Journal of Cleaner Production, 18(12), 1200–1210. https://doi.org/10.1016/j.jclepro.2010.01.01 6 Bocken, N., Strupeit, L., Whalen, K., Nußholz, J., 2019, A review and evaluation of circular business model innovation tools, In Sustainability (11(8), 2210), Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/su11082210 Bowen, F., Cousins, P., Lamming, R., Faruk, A., 2006, Horses for courses: Explaining the gap between the theory and practice of green supply, In Greening the Supply Chain (151– 172), Springer London. https://doi.org/10.1007/1-84628-299-3_9 Chien, M. K., Shih, L. H., 2007, An empirical study of the implementation of green supply chain management practices in the electrical and electronic industry and their relation to organizational performances, International Journal of Environmental Science and Technology, 4(3), 383–394. https://www.sid.ir/en/journal/ViewPaper.as px?id=77181 Dubey, R., Bag, S., Ali, S. S., Venkatesh, V. G., 2013, Green purchasing is key to superior performance: An empirical study, International Journal of Procurement Management, 6(2), 187–210. https://doi.org/10.1504/IJPM.2013.052469 ElTayeb, T. K., Zailani, S., Jayaraman, K., 2010, The examination on the drivers for green purchasing adoption among EMS 14001 certified companies in Malaysia, Journal of Manufacturing Technology Management, 21(2), 206–225. https://doi.org/10.1108/1741038101101437 8 Ferri, L. M., Pedrini, M., 2018, Socially and environmentally responsible purchasing: Comparing the impacts on buying firm’s financial performance, competitiveness and risk, Journal of Cleaner Production, 174, 880–888. https://doi.org/10.1016/j.jclepro.2017.11.03 5 Foerstl, K., Reuter, C., Hartmann, E., Blome, C., 2010, Managing supplier sustainability risks in a dynamically changing environment-Sustainable supplier management in the chemical industry, Journal of Purchasing and Supply Management, 16(2), 118–130. https://doi.org/10.1016/j.pursup.2010.03.01 1 González-Benito, J., Lannelongue, G., Ferreira, L. M., Gonzalez-Zapatero, C., 2016, The effect of green purchasing on purchasing performance: the moderating role played by long-term relationships and strategic integration, Journal of Business and Industrial Marketing, 31(2), 312–324. https://doi.org/10.1108/JBIM-09-2014- 0188 Govindan, K., Shankar, K. M., Kannan, D., 2020, Achieving sustainable development goals through identifying and analyzing barriers to industrial sharing economy: A framework development, International Journal of Production Economics, 227, 107575. https://doi.org/10.1016/j.ijpe.2019.107575 Green, K. W., Zelbst, P. J., Meacham, J., Bhadauria, V. S., 2012, Green supply chain management practices: Impact on performance, Supply Chain Management, 17(3), 290–305. https://doi.org/10.1108/1359854121122712 6 Guide, V. D. R., Van Wassenhove, L. N., 2009, The evolution of closed-loop supply chain research, Operations Research, 57(1), 10– 18. https://doi.org/10.1287/opre.1080.0628 Kalinowski, T. B., Rudnicka, A., Wieteska, G., Wronka, A., Diglio, A., Piccolo, C., Bruno, G., Solomon, A., Koh, S. C. L., Genovese, A., 2019, Competences Required for environmentally responsible managers - a European perspective, Proceedings of International Academic Conferences. http
green supply chain management, green purchasing, green supplier development, green concepts, supply chain management, environmental impacts
10.17270/J.LOG.2022.680
Current trends in the German packaging industry
ORIGINAL_ARTICLE
27-33
en
2022
18
1
Paolo
Gavazzi
Renata
Dobrucka
Robert
Przekop
Bailey, R., Boucher, J., Boughton, J., Castillo, A., Da, M., Favoino, E., Gadgil, M., Godfrey, L., Gutberlet, J., Kosior, E., Lao, C., Lerario, D., Moss, E., Russo, D., Sumaila, U., Thompson, R., Velis, C., 2021. Breaking the Plastic Wave - A comprehensive assessment of pathways towards stopping ocean plastic pollution. Systemiq, London. Blass, P., Feeß, K., 2021. Kreislaufwirtschaft, E-Commerce, Digitalisierung - Die Verpackungsindustrie steht unter dem Einfluss starker Strömungen, kann aber auch viele Bereiche neu gestalten [Circular economy, e-commerce, digitalization - The packaging industry is under the influence of strong currents but can also reshape many areas]. Fachpack, Nürnberg. Carling, K., Han, M., Håkansson, J., Meng, X., Rudholm, N., 2015. Measuring transport related CO 2 emissions induced by online and brick-and-mortar retailing. Transp. Res. Part Transp. Environ. 40, 28–42, https://doi.org/10.1016/j.trd.2015.07.010 .Chen, S., Brahma, S., Mackay, J., Cao, C., Aliakbarian, B., 2020. The role of smart packaging system in food supply chain. J. Food Sci. 85, 517–525, https://doi.org/10.1111/1750-3841.15046 Coelho, P.M., Corona, B., ten Klooster, R., Worrell, E., 2020. Sustainability of reusable packaging–Current situation and trends. Resour. Conserv. Recycl. X 6, 100037, https://doi.org/10.1016/j.rcrx.2020.100037 Dobrucka, R., 2013. The future of active and intelligent packaging industry. Logforum 9(2), 103–110. Dobrucka, R., 2019. Bioplastic Packaging Materials in Circular Economy. Logforum 15(1), 129–137, http://doi.org/10.17270/J.LOG.2019.322 Drago, E., Campardelli, R., Pettinato, M., Perego, P., 2020. Innovations in Smart Packaging Concepts for Food: An Extensive Review. Foods 9, 1628, https://doi.org/10.3390/foods9111628 Ellen MacArthur foundation 2021, Circular design resource. Available from Internet https://ellenmacarthurfoundation.org/resour ces/design/overview Escursell, S., Llorach-Massana, P., Roncero, M.B., 2021. Sustainability in e-commerce packaging: A review. J. Clean. Prod. 280, 124314, https://doi.org/10.1016/j.jclepro.2020.1243 14 Esser, K., Kurte, J., 2021. Kurier Express Paketdienste (KEP)-Studie 2021 – Analyse des Marktes in Deutschland [Courier Express Parcel Services (CEP) Study 2021 - Analysis of the Market in Germany]. Bundesverband Paket & Express Logistik BIEK, Köln. Fuhr, L., Buschmann, R., Freund, J. (Eds.), 2019. Plastikatlas: Daten und Fakten über eine Welt voller Kunststoff [Plastics: Facts and figures about a world full of plastic], 2. Aufl. ed. Heinrich-Böll-Stiftung, Berlin. Geissdoerfer, M., Pieroni, M.P.P., Pigosso, D.C.A., Soufani, K., 2020. Circular business models: A review. J. Clean. Prod. 277, 123741, https://doi.org/10.1016/j.jclepro.2020.1237 41 Herrmann, S., Kast, M., Philipp, F., Stuchtey, M., 2021. Verpackungswende jetzt! So gelingt der Wandel zu einer Kreislaufwirtschaft für Kunststoffe in Deutschland [Packaging turnaround. How to make the transition to a circular economy for plastics in Germany]. WWF Deutschland. Kirchherr, J., Reike, D., Hekkert, M., 2017. Conceptualizing the circular economy: An ana
Circular Economy, Sustainable development, Packaging Industry, Trends
10.17270/J.LOG.2022.688
Supporting of manufacturing system based on demand forecasting tool
ORIGINAL_ARTICLE
35-50
en
2022
18
1
Mariusz
Kmiecik
Hawre
Zangana
Baryannis G., Validi S., Dani S., Antoniou G., 2019. Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, Vol. 57 No. 7, pp. 2179-2202. https://doi.org/10.1080/00207543.2018.1530476
Burtch L, 2016. SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? KDnuggets. Retrieved 30 December 2020, from https://www.kdnuggets.com/2016/07/burtchworks-sas-r-python-analytics-pros-prefer.html
Chandra C., Grabis J., 2004. Supply Chain Configuration, Concepts, Solutions, and Applications. Springer. http://doi.org/10.1007/978-1-4939-3557-4
Chase C., 2009. Demand-Driven Forecasting: A Structured Approach to Forecasting. Hoboken, NJ: Wiley & Sons.
Chase C., 2016. Next generation demand management. Wiley.
Czwajda L., Kosacka-Olejnik M., Kudelska I., Kostrzewski M., Sethanan K., Pitakaso R., 2019. Application of prediction markets phenomenon as decision support instrument in vehicle recycling sector. LogForum vol. 15, pp.266-278. http://doi.org/10.17270/J.LOG.2019.329
Dangerfield B. J., Morris J. S., 1992. Top-down or bottom-up: Aggregate versus disaggregate extrapolations. International Journal of Forecasting, 8(2), 233–241. https://doi.org/10.1016/0169-2070(92)90121-O
Dangerfield B.J., Morris J.S., 1988. An empirical evaluation of top-down and bottom-up forecasting strategies. Proceedings of the 1988 Meeting of Western Decision Sciences Institute. 322-324.
Davenport TH., Ronanki R., 2018. Artificial intelligence for the real world. Harvard Bus Rev 96:108–116
David F., 2011. Strategic management. Prentice Hall.
Dubey R., Gunasekaran A., Childe S.J., Bryde, D.J., Giannakis M., Foropon C., Hazen, B.T., 2020. Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: a study of manufacturing organizations. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.107599
Dujak D., Sebalj D., Koliński A., 2019. Towards exploring bullwhip effect in natural gas supply chain. LogForum vol.15, pp.51-62. http://doi.org/10.17270/J.LOG.2019.369
Dwivedi Y.K., Hughes L., Ismagilova E., Aarts G., Coombs C., Crick T., Duan Y., Dwivedi R., Edwards J., Eirug A., Galanos V., Ilavarasan P.V., Janssen M., Jones P., Kar A.K., Kizgin H., Kronemann B., Lal B., Lucini B., Williams M.D., 2019. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fogarty D., Blackstone J., Hoffmann T., 1994. Production & inventory management. South-Western Publishing Co.
Gische Ch., West S.G., Voelkle M.C., 2020. Forecasting Causal Effects of Interventions versus Predicting Future Outcomes. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2020.1780598
Gunasekaran A., Papadopoulos T., Dubey R., Wamba S.F., Childe S.J., Hazen B., Akter S., 2017. Big data and predictive analytics for supply chain and organizational performance. J Bus Res 70:308–317. https://doi.org/10.1016/j.jbusres.2016.08.004
Gupta S., Modgil S., Bhattacharyya S., Bose I., 2021. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03856-6
Huang G., Zhu Q., Siew C., 2004. Extreme learning machine: a new learning scheme of feedforward neural networks. 2004 IEEE International Joint Conference on Neural Networks, IEEE Cat. No.04CH37541, 2, 985-990 vol.2. https://doi.org/10.1109/IJCNN.2004.1380068
Hughes P., and Morgan N., 1967. The Use of Computers in Forecasting. Journal of the Royal Statistical Society. Series D, The Statistician, 17(3), 279-299. https://doi.org/10.2307/2986721.
Hyndman R.J., Athanasopoulos G., 2018. Forecasting: Principles and Practice. OTexts.
Ivanov D., 2020. Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, Vol. 136, 101922. https://doi.org/10.1016/j.tre.2020.101922.
Khandelwal I., Adhikari R., Verma G., 2015. Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition. Procedia Computer Science Volume 48, Pages 173-179. https://doi.org/10.1016/j.procs.2015.04.167.
Krzyżaniak S., 2017. Influence of delivery quantity on service level in the stock replenishment system based on reorder level for irregular distributions of demand. LogForum vol. 13. http://dx.doi.org/10.17270/J.LOG.2017.4.3
Lee M.K., 2018. Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc 5:1–16. https://doi.org/10.1177%2F2053951718756684
Li P., Ma C., Ning J., Wang Y., Zhu C., 2019. Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations. Sustainability, 11(19), 5281. MDPI AG. Retrieved from http://dx.doi.org/10.3390/su11195281
Liu H., Mi X., Li Y., 2018. An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm. Renewable Energy, Volume 123, Pages 694-705. https://doi.org/10.1016/j.renene.2018.02.092
Mahbub N., Paul M., 2013. A neural approach to product demand forecasting. International Journal of Industrial and Systems Engineering 15(1):1-18. http://dx.doi.org/10.1504/IJISE.2013.055508
Malladi K.T., Sowlati T., 2018. Biomass logistics: A review of important features, optimization modeling and the new trends. Renewable and Sustainable Energy Reviews Volume 94, Pages 587-599. https://doi.org/10.1016/j.rser.2018.06.052
Mesjasz L., 2011. Forecasting of demand for direct production materials as the element of supply logistics of thermal power plants. LogForum vol. 7, pp.51-61.
MLP function - RDocumentation. 2021. Rdocumentation.org. Retrieved 5 January 2021, from https://www.rdocumentation.org/packages/nnfor/versions/0.9.6/topics/mlp
Modgil S., Singh R., Hannibal C., 2021. Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management. https://doi.org/10.1108/ijlm-02-2021-0094
Muniz L.R., Conceição S.V., Rodrigues L.F., de Freitas Almeida J.F., Affonso T.B., 2020. Spare parts inventory management: a new hybrid approach. International Journal of Logistics Management, Vol. 32 No. 1, pp. 40-67. http://dx.doi.org/10.1108/IJLM-12-2019-0361
Narasimhan S., McLeavey D., Billington P., 2007. Production planning and inventory control. Prentice-Hall of India.
Niazkar M., Turrkan G.E., Niazkar H.R. and Turrkan Y.A., 2020. Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey. Computional and Mathematical Methods in Medicine. https://doi.org/10.1155/2020/7056285
Orcutt G., Watts H., Edwards J., 1968. Data Aggregation and Information Loss. The American Economic Review, 58(4), 773-787. http://www.jstor.org/stable/1815532
Phan P., Wright M., Soo-Hoon L., 2017. Of robots, artificial intelligence, and work. Academy of Management Perspectives Vol. 31, No. 4 31:253–255. https://doi.org/10.5465/amp.2017.0199
Posen H.E., Levinthal D.A., 2012. Chasing a Moving Target: Exploitation and Exploration in Dynamic Environments. Management Science. 58. https://doi.org/10.1287/mnsc.1110.1420
Szozda N., Werbyńska-Wojciechowska S., 2013. Influence of the demand information quality on planning process accuracy in supply chain. Case studies, LogForum vol.9.
Tarallo E., Akabane G., Shimabukuro C., Mello J., Amancio D., 2019. Machine Learning in Predicting Demand for Fast-Moving Consumer Goods: An Exploratory Research. IFAC-Papersonline, 52(13), 737-742. https://doi.org/10.1016/j.ifacol.2019.11.203
Vokhmyanina A., Zhuravskaya M., Osmólski W., 2018. The issue of bullwhip-effect evaluating in supply chain management, LogForum vol. 13. http://dx.doi.org/10.17270/J.LOG.280
Wamba S.F., Queiroz M.M., Wu L., Sivarajah U., 2020. Big data analytics-enabled sensing capability and organizational outcomes: assessing the mediating effects of business analytics culture. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03812-4
Zhang G., Patuwo B.E., Hu, M.Y., 1998. Forecasting with artificial neural networks. International Journal of Forecasting, 14(1), 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7
demand forecasting, R Studio, ARIMA model, Neural Network model, Machine learning model, manufacturing system
10.17270/J.LOG.2022.637
Influence of reverse logistics on competitiveness, economic performance, ecological environment and society
ORIGINAL_ARTICLE
51-60
en
2022
18
1
Tetiana
Ivanova
Robert
Rogaczewski
Iryna
Lutsenko
Aksoylu S., Demirel N., 2018. Application of Activity Based Costing in Reverse Logistics Environment: A Case of End-of-life Vehicle Recovery in Turkey. Journal of Business Research-Turk, 10, 953-973. https://doi.org/10.20491/isarder.201
Reverse Logistics, Competitiveness, Economic performance, Ecological environment, Society JEL Classification: F63, L23, O14
10.17270/J.LOG.2022.640
The effects of information technologies on automotive supply chain and firm performance
ORIGINAL_ARTICLE
61-75
en
2022
18
1
Omar
Boubker
Acar, A.Z., Uzunlar, M.B., 2014. The Effects of Process Development and Information Technology on Time-based Supply Chain Performance. Procedia - Social and Behavioral Sciences, 150: 744–753. https://doi.org/10.1016/j.sbspro.2014.09.04 4 Ataseven, C., Nair, A., Ferguson, M., 2020. The role of supply chain integration in strengthening the performance of not-for profit organizations: evidence from the food banking industry. Journal of Humanitarian Logistics and Supply Chain Management, 10(2): 101–123. https://doi.org/10.1108/JHLSCM-04-2019- 0024 Bal, M., Pawlicka, K., 2021. Supply chain finance and challenges of modern supply chains. LogForum, 17(1): 71-82. http://doi.org/10.17270/J.LOG.2021.525 Balambo, M. A., 2013. Culture nationale et nature de l’intégration des supply chains amont: le cas des équipementiers automobiles marocains. Logistique & Management, 21(4): 71-85. http://dx.doi.org/10.1080/12507970.2013.1 1517036 Barratt, M., Barratt, R., 2011. Exploring internal and external supply chain linkages: Evidence from the field. Journal of Operations Management, 29(5), 514–528. https://doi.org/10.1016/j.jom.2010.11.006 Barros, A.P. de, Ishikiriyama, C.S., Peres, R.C., Gomes, C.F.S., 2015. Processes and Benefits of the Application of Information Technology in Supply Chain Management: An Analysis of the Literature. Procedia Computer Science, 55: 698-705. https://doi.org/10.1016/j.procs.2015.07.077 Basnet, C., 2013. The measurement of internal supply chain integration. Management Research Review, 36(2): 153–172. https://doi.org/10.1108/0140917131129225 2 Boon-itt, S., 2009. The effect of internal and external supply chain integration on product quality and innovation: evidence from Thai automotive industry. International Journal of Integrated Supply Management, 5(2): 97– 112.https://doi.org/10.1504/IJISM.2009.029 356 Boubker, O., Douayri, K., 2020. Dataset on the relationship between consumer satisfaction, brand attitude, brand preference and purchase intentions of dairy product: The case of the Laayoune-Sakia El Hamra region in Morocco. Data in Brief, 32: 106172. https://doi.org/10.1016/j.dib.2020.106172 Boubker, O., Douayri, K., Ouajdouni, A., 2021. Factors affecting intention to adopt Islamic financing: Evidence from Morocco. MethodsX, 8: 101523. https://doi.org/10.1016/j.mex.2021.101523 Boysen, N., Emde, S., Hoeck, M., Kauderer, M., 2015. Part logistics in the automotive industry: Decision problems, literature review and research agenda. European Journal of Operational Research, 242(1): 107–120. https://doi.org/10.1016/j.ejor.2014.09.065 Chang, W., Ellinger, A.E., Kim, K., Franke, G.R., 2016. Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors.European Management Journal, 34(3): 282–295. https://doi.org/10.1016/j.emj.2015.11.008 Chen, C.-J., 2019. Developing a model for supply chain agility and innovativeness to enhance firms’ competitive advantage. Management Decision, 57(7): 1511-1534. https://doi.org/10.1108/MD-12-2017-1236 Chen, M., Liu, H., Wei, S., Gu, J., 2018. Top managers’ managerial ties, supply chain integration, and firm performance in China: A social capital perspective. Industrial Marketing Management, 74: 205–214. https://doi.org/10.1016/j.indmarman.
Supply chain, information technology, IT integration, information management, performance
10.17270/J.LOG.2022.641
Fuzzy failure mode and effect analysis model for operational supply chain risks assessment: an application in canned tuna manufacturer in Thailand
ORIGINAL_ARTICLE
77-96
en
2022
18
1
Detcharat
Sumrit
Sirima
Srisawad
Butdee S., Phuangsalee P., 2019. Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP, Procedia Manufacturing, 30, 663– 670. https://doi.org/10.1016/j.promfg.2019.02.094 da Silva C., Barbosa-P
Operational supply chain risks; FMEA; MCDM; Shannon entropy; VIKOR
10.17270/J.LOG.2022.645
Flight delay prediction based with machine learning
ORIGINAL_ARTICLE
97-107
en
2022
18
1
Irmak
Hatıpoğlu
Ömür
Tosun
Nedret
Tosun
Abdelghany, K. F., Shah, S. S., Raina, S., & Abdelghany, A. F., 2004. A model for projecting flight delays during irregular operation conditions. Journal of Air Transport Management, 10, 385-394. http://doi.org/10.1016/j.jairtraman.2004.06. 008 Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P., 2002. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of artificial intelligence research, 16: 321-357. https://dl.acm.org/doi/10.5555/1622407.162 2416 Chen, J., & Li, M., 2019. Chained Predictions of Flight Delay Using Machine Learning. AIAA SciTech Forum, San Diego: American Institute of Aeronautics and Astronautics, Inc. 1-25. https://doi.org/10.2514/6.2019-1661 Chen, T., & Guestrin, C., 2016]. Xgboost: a scalable tree boosting system. Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining, 785-794. https://dl.acm.org/doi/10.1145/2939672.293 9785 Choi, S., Kim, Y. K., Briceno, S., & Mavris, D., 2016. Prediction of Weather-induced Airline Delays Based on Machine Learning Algorithms. 35th Digital Avionics Systems Conference, 1-6, IEEE. https://doi.org/10.1109/DASC.2016.777795 6 Cohen, J., 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1): 37-46. https://doi.org/10.1177%2F0013164460020 00104 Dorogush, A. V., Ershov, V., & Gulin, A., 2018. CatBoost: gradient boosting with categorical features support. arXiv preprint, 1-7. https://arxiv.org/abs/1810.11363 Dray, L. M., Antony, E., Vera-Morales, M., Reynolds, T. G., & Schafer, A., 2008. Network and Environmental Impacts of Passenger and Airline Response to Cost and Delay. 8th AIAA Aviation Technology, Integration and Operations Conference, 8890-8901. Anchorage. https://doi.org/10.2514/6.2008-8890 Efthymiou, M., Njoya, E. T., Lo, P. L., Papatheodorou, A., & Randall, D., 2019. The Impact of Delays on Customers' Satisfaction: an Empirical Analysis of the British Airways On-Time Performance at Heathrow Airport. Journal of Aerospace Technology and Managemen
GBDT, XGBoost, LightGBM, Catboost, delay prediction
10.17270/J.LOG.2022.655
Exploring real-time visibility transportation platform deployment
ORIGINAL_ARTICLE
109-121
en
2022
18
1
Sławomir
Wyciślak
Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G., 2021, Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, 108193. https://doi.org/10.1016/j.ijpe.2021.108193
Arcos-García, Á., Álvarez-García, J. A., & Soria-Morillo, L. M., 2018, Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods. Neural Networks, 99, 158-165, https://doi.org/10.1016/j.neunet.2018.01.005
Babatunde, S., Oloruntoba, R., & Agho, K., 2020, Healthcare commodities for emergencies in Africa: review of logistics models, suggested model and research agenda. Journal of Humanitarian Logistics and Supply Chain Management. https://doi.org/10.1108/JHLSCM-09-2019-0064
Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K., 2015, Big Data Analytics in Healthcare. BioMed Research International, 370194, https://doi.org/10.1155/2015/370194
Celikoglu, H. B., 2011, Travel time measure specification by functional approximation: application of radial basis function neural networks. Procedia - Social and Behavioral Sciences, 20(0), 613-620, https://doi.org/10.1016/j.sbspro.2011.08.068
Dossou, P.-E., Foreste, L., & Misumi, E., 2021, Intelligent Support System for Healthcare Logistics 4.0 Optimization in the Covid Pandemic Context. Journal of Software Engineering and Applications, 14(6), 233-256. https://doi.org/10.4236/jsea.2021.146014
Galetsi, P., & Katsaliaki, K., 2020, A review of the literature on big data analytics in healthcare. Journal of the Operational Research Society, 71(10), 1511-1529, https://doi.org/10.1080/01605682.2019.1630328
Gawrońska, A., & Nowak, F., 2017, Modelling medicinal products inventory management process in hospitals using a methodology based on the BPMN standard. Logforum, 13(4), 6. https://doi.org/10.17270/J.LOG.2017.4.6
Ghaderzadeh, M., & Asadi, F., 2021, Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review, Journal of Healthcare Engineering, 2021, 6677314, https://doi.org/10.1155/2021/6677314
Granillo-Macías, R., 2020, Inventory management and logistics optimization: a data mining practical approach, LogForum, 16(4)
Ha, Q. P., Wahid, H., Duc, H., & Azzi, M., 2015, Enhanced radial basis function neural networks for ozone level estimation, Neurocomputing, 155(0), 62-70, https://doi.org/10.1016/j.neucom.2014.12.048
Ilati, M., & Dehghan, M., 2015, The use of radial basis functions (RBFs) collocation and RBF-QR methods for solving the coupled nonlinear sine-Gordon equations. Engineering Analysis with Boundary Elements, 52(0), 99-109, https://doi.org/10.1016/j.enganabound.2014.11.023
Inanç, Ş., & Şenaras, A. E., 2020, An application for routing ambulance via ACO in home healthcare, In: transportation, logis
Kergosien, Y., Lenté, C., Billaut, J.-C., & Perrin, S., 2013, Metaheuristic algorithms for solving two interconnected vehicle routing problems in a hospital complex. Computers & Operations Research, 40(10), 2508-2518. https://doi.org/10.1016/j.cor.2013.01.009
Khanra, S., Dhir, A., Islam, A. K. M. N., & Mäntymäki, M., 2020, Big data analytics in healthcare: a systematic literature review. Enterprise Information Systems, 14(7), 878-912. https://doi.org/10.1080/17517575.2020.1812005
Kritchanchai, D., Krichanchai, S., Hoeur, S., & Tan, A., 2019, Healthcare supply chain management: macro and micro perspectives. LogForum, 15(4). https://doi.org/10.17270/J.LOG.2019.371
Lapierre, S. D., & Ruiz, A. B., 2007, Scheduling logistic activities to improve hospital supply systems. Computers & Operations Research, 34(3), 624-641. https://doi.org/10.1016/j.cor.2005.03.017
Lee, C. H., & Yoon, H.-J., 2017, Medical big data: promise and challenges. Kidney research and clinical practice, 36(1), 3. https://doi.org/10.23876/j.krcp.2017.36.1.3
Majchrzak-Lepczyk, J., & Bober, B., 2016, Selected aspects of the logistics network of public hospitals in the competitive market of health services. Logforum, 12(4), 6. https://doi.org/10.17270/J.LOG.2016.4.6
Raghupathi, W., & Raghupathi, V., 2014, Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. https://doi.org/10.1186/2047-2501-2-3
Setzler III, H. H., 2007, Developing an accurate forecasting model for temporal and spatial ambulance demand via artificial neural networks: A comparative study of existing forecasting techniques vs. an artificial neural network, The University of North Carolina at Charlotte.
Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Á., 2019, Decision-Making based on Big Data Analytics for People Management in Healthcare Organizations. Journal of Medical Systems, 43(9), 290. https://doi.org/10.1007/s10916-019-1419-x
Tlili, T., Abidi, S., & Krichen, S., 2018, A mathematical model for efficient emergency transportation in a disaster situation. The American journal of emergency medicine, 36(9), 1585-1590. https://doi.org/10.1016/j.ajem.2018.01.039
von Elmbach, A. F., Scholl, A., & Walter, R., 2019, Minimizing the maximal ergonomic burden in intra-hospital patient transportation. European Journal of Operational Research, 276(3), 840-854. https://doi.org/10.1016/j.ejor.2019.01.062
Wajid, S., Nezamuddin, N., & Unnikrishnan, A., 2020, Optimizing Ambulance Locations for Coverage Enhancement of Accident Sites in South Delhi. Transportation Research Procedia, 48, 280-289. https://doi.org/10.1016/j.trpro.2020.08.022
Yang, W., Su, Q., Huang, S. H., Wang, Q., Zhu, Y., & Zhou, M., 2019, Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand. Journal of Management Science and Engineering, 4(4), 252-265. https://doi.org/10.1016/j.jmse.2020.01.004
Yang, Y., Cao, M., Cheng, L., Zhai, K., Zhao, X., & De Vos, J., 2021, Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study. Transportation Research Interdisciplinary Perspectives, 11, 100450. https://doi.org/10.1016/j.trip.2021.100450
supply chain visibility, transportation visibility platform, supply chain, freight forwarders, digitization
10.17270/J.LOG.2022.660
A bibliometric analysis of the application of social network analysis in supply chain management
ORIGINAL_ARTICLE
123-136
en
2022
18
1
Can
Wang
Alinaghian L., Qiu J., Razmdoost K., 2020. The role of network structural properties in supply chain sustainability: a systematic literature review and agenda for future research. Supply Chain Management: An International Journal, 26(2): 192-211. https://doi.org/10.1108/SCM-11-2019-0407 Bellamy M.A., Ghosh S., Hora M., 2014. The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6): 357-373. https://doi.org/10.1016/j.jom.2014.06.004 Bing L., 2011. Social Network Analysis. Springer Berlin Heidelberg. Bode C., Wagner S.M., 2015. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36: 215- 228. https://doi.org/10.1016/j.jom.2014.12.004 Borgatti S., Li X., 2009. On social network analysis in a supply chain context. Social Science Electronic Publishing, 45(2): 5-22. https://doi.org/10.1111/j.1745- 493X.2009.03166.x Brandenburg M., Govindan K., Sarkis J., Seuring S., 2014. Quantitative models for sustainable supply chain management: developments and directions. European Journal of Operational Research, 233(2): 299-312. https://doi.org/10.1016/j.ejor.2013.09.032 Carter C.R., Rogers D.S., Choi T.Y., 2015. Toward the theory of the supply chain. Journal of Supply Chain Management, 51(2): 89-97. https://doi.org/10.1111/jscm.12073 Chaabane A., Ramudhin A., Paquet M., 2012. Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1): 37-49. https://doi.org/10.1016/j.ijpe.2010.10.025 Chen C., 2004. Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101: 5303–5310. https://doi.org/10.1073/pnas.0307513100 Chen C., 2006. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3): 359–377. https://doi.org/10.1002/asi.20317 Chen C., 2017. Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science, 2(2): 1-40. https://doi.org/10.1515/jdis-2017-0006 Choi T.Y., Kim Y., 2010. Structural embeddedness and supplier management: a network perspective. Journal of Supply Chain Management, 44(4): 5-13. https://doi.org/10.1111/j.1745- 493X.2008.00069.x Choi T.Y., Wu Z.H., 2009. Triads in supply networks: theorizing buyer-supplier supplier relationships. Journal of Supply Chain Management, 45(1): 8-25. https://doi.org/10.1111/j.1745- 493X.2009.03151.x Devika K., Jafarian A., Nourbakhsh V., 2014. Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3): 594–615. https://doi.org/10.1016/j.ejor.2013.12.032 Eskandarpour M., Dejax P., Miemczyk J., Peton O., 2015. Sustainable supply chain network design: an optimization-oriented review. Omega, 54: 11-32. https://doi.org/10.1016/j.omega.2015.01.00 6 Fahimnia B., Sarkis J., Davarzani H., 2015. Green supply chain management: a review and bibliometric analysis. International Journal of Production Economics, 162: 101- 114. https://doi.org/10.1016/j.ijpe.2015.01.003 Fursov K.S., Kadyrova A.R., 2017. How the analysis of transitionary references in knowledge networks and their centrality characteristics helps in understandin
social network analysis, supply chain management, bibliometric analysis, CiteSpace
10.17270/J.LOG.2022.676